learningcircuit's comments

Local Deep Research is an open-source tool that enables deep, iterative research using AI without sending your queries to external services. It combines language models with specialized search capabilities to conduct multi-stage research that builds on initial findings. Key capabilities include:

Discovering connections between sources that simple search misses Producing structured reports with proper citations, not just collected snippets Integrating your personal document library as a research source Cross-referencing your documents with web sources in a single research flow Easily switching between search engines including PubMed, arXiv, and SearXNG Working entirely offline with local LLMs (via Ollama)

SearXNG integration is particularly powerful as it provides a high-quality, privacy-focused search experience without requiring API keys. The 0.2.0 release adds a redesigned UI, unified settings database, parallel search for faster results, and improved error recovery. With over 600 commits and 5 core contributors, the project is actively growing, and we're looking for more contributors to join the effort. Getting involved is straightforward even for those new to the codebase. More details: https://www.reddit.com/r/LocalDeepResearch/ GitHub: https://github.com/LearningCircuit/local-deep-research

I wanted to share some updates on Local Deep Research since posting here 12 days ago. Thanks to everyone who gave feedback and suggestions - the project has improved significantly with your input.

Recent improvements:

- Better inline citation: Sources from PubMed, arXiv, Wikipedia, etc. are now properly cited directly in the text

- Improved report structure: Reports now have better organization with logical sections and clearer source attribution

- Added support for multiple research domains: Works well across scientific, historical, economic, and technical topics

- Enhanced search iterations: Now performs multiple rounds of research with follow-up questions for deeper analysis - More flexible LLM integration: Works with pretty much any model (local via Ollama or cloud-based)

- Expanded search engine options: Easy to add new sources for specialized research

For those who mentioned concerns about report quality and organization - we've made significant improvements in this area. The citation tracking now provides much better provenance information throughout the research pipeline.

I'd also like to thank HashedViking who joined as a contributor and has been improving the UI/UX side of things. We're committed to keeping this as a truly local, privacy-focused tool that doesn't rely on expensive APIs.

For anyone interested in contributing, we're looking for help with: 1. Further improving report organization 2. More local search engines and sources 3. Documentation and examples 4. UI/UX enhancements 5. Testing with different models and research domains

The project is at: https://github.com/LearningCircuit/local-deep-research/

What features would be most useful to you in a research tool like this? We're particularly interested in ideas for better knowledge organization and making the research outputs more valuable.

If you have PDF collection you could include it into the local search and give it very high relevance?

I will add it. Its very easy to integrate new search engines.

Very good ideas I will try to include them.

Thinking models... You can use them. In fact I started the project with them but not sure they help too much for this task. They definitely make it slower

Give me a question and I can give you the output? So you can compare.

Thank you I will add

Very good answer. It is very hard with small LLM.

How do they evaluate the quality of the report? It's one of the most important things for me.

Example output: https://github.com/LearningCircuit/local-deep-research/blob/...